Our group’s research centers around the development of robust machine learning methods, with major focus on mining and learning principles for graphs and networks.
Since in many real-world applications the collected data is rarely of high-quality but often noisy, prone to errors, or vulnerable to manipulations, robustness of algorithms is crucial to ensure reliable results. Therefore, our goal is to design techniques which handle different forms of errors and corruptions in an automatic way. In this regard, our group is especially interested in designing techniques for non-independent data: While one of the most common assumptions in many machine learning and data analysis tasks is that the given data points are realizations of independent and identically distributed random variables, this assumption is often violated. Sensors are interlinked with each other in networked cyber physical systems, people exchange information in social networks, and molecules or proteins interact based on biochemical events. In our research, we exploit these dependencies by developing learning methods for, e.g., graphs and network data.
We are hiring!
Please check out our open positions for PhD students/PostDocs!
Additionally, we are offering multiple student assistant (HiWi/Tutor) positions for our Machine Learning lecture